Two new grants from the Dutch Cancer Society

We received two grant from the Dutch Cancer Society to study:

Lithium for the prevention of polyps
Louis Vermeulen, together with co-applicants Sanne van Neerven and Evelien Dekker, will investigate whether it is possible to prevent polyp formation in patients with familial adenomatous polyposis (FAP). Preliminary research showed that lithium effectively disrupts polyp formation. Lithium is a cheap drug that is often prescribed for patients with schizophrenia. In an exploratory study, Vermeulen will examine whether lithium is effective in preventing polyps in a small group of FAP patients.

Treatment of metastases to the peritoneum
In  a project led by Maarten Bijlsma, the Vermeulen lab is researching treatment options for peritoneal metastases. The team, together with the lab of Onno Kranenburg (UMC Utrecht)  examines the molecular properties of these tumors to identify (combinations of) drugs that are effective against them. If successful in the laboratory, the tailored treatment will be tested in future clinical trials.

For more information visit the site of Amsterdam UMC:

Predictors of 30-Day Mortality Among Dutch Patients Undergoing Colorectal Cancer Surgery, 2011-2016.

Importance: Quality improvement programs for colorectal cancer surgery have been introduced with benchmarking based on quality indicators, such as mortality. Detailed (pre)operative characteristics may offer relevant information for proper case-mix correction.

Objective: To investigate the added value of machine learning to predict quality indicators for colorectal cancer surgery and identify previously unrecognized predictors of 30-day mortality based on a large, nationwide colorectal cancer registry that collected extensive data on comorbidities.

Design, setting, and participants: All patients who underwent resection for primary colorectal cancer registered in the Dutch ColoRectal Audit between January 1, 2011, and December 31, 2016, were included. Multiple machine learning models (multivariable logistic regression, elastic net regression, support vector machine, random forest, and gradient boosting) were made to predict quality indicators. Model performance was compared with conventionally used scores. Risk factors were identified by logistic regression analyses and Shapley additive explanations (ie, SHAP values). Statistical analysis was performed between March 1 and September 30, 2020.

Main outcomes and measures: The primary outcome of this cohort study was 30-day mortality. Prediction models were trained on a training set by performing 5-fold cross-validation, and outcomes were measured by the area under the receiver operating characteristic curve on the test set. Machine learning was further used to identify risk factors, measured by odds ratios and SHAP values.

Results: This cohort study included 62 501 records, most patients were male (35 116 [56.2%]), were aged 61 to 80 years (41 560 [66.5%]), and had an American Society of Anesthesiology score of II (35 679 [57.1%]). A 30-day mortality rate of 2.7% (n = 1693) was found. The area under the curve of the best machine learning model for 30-day mortality (0.82; 95% CI, 0.79-0.85) was significantly higher than the American Society of Anesthesiology score (0.74; 95% CI, 0.71-0.77; P < .001), Charlson Comorbidity Index (0.66; 95% CI, 0.63-0.70; P < .001), and preoperative score to predict postoperative mortality (0.73; 95% CI, 0.70-0.77; P < .001). Hypertension, myocardial infarction, chronic obstructive pulmonary disease, and asthma were comorbidities with a high risk for increased mortality. Machine learning identified specific risk factors for a complicated course, intensive care unit admission, prolonged hospital stay, and readmission. Laparoscopic surgery was associated with a decreased risk for all adverse outcomes.

Conclusions and relevance: This study found that machine learning methods outperformed conventional scores to predict 30-day mortality after colorectal cancer surgery, identified specific patient groups at risk for adverse outcomes, and provided directions to optimize benchmarking in clinical audits.

Mimicking and surpassing the xenograft model with cancer-on-chip technology.

Organs-on-chips are in vitro models in which human tissues are cultured in microfluidic compartments with a controlled, dynamic micro-environment. Specific organs-on-chips are being developed to mimic human tumors, but the validation of such ‘cancer-on-chip’ models for use in drug development is hampered by the complexity and variability of human tumors. An important step towards validation of cancer-on-chip technology could be to first mimic cancer xenograft models, which share multiple characteristics with human cancers but are significantly less complex. Here we review the relevant biological characteristics of a xenograft tumor and show that organ-on-chip technology is capable of mimicking many of these aspects. Actual comparisons between on-chip tumor growth and xenografts are promising but also demonstrate that further development and empirical validation is still needed. Validation of cancer-on-chip models to xenografts would not only represent an important milestone towards acceptance of cancer-on-chip technology, but could also improve drug discovery, personalized cancer medicine, and reduce animal testing.